Summary of On the Convergence Of Continual Federated Learning Using Incrementally Aggregated Gradients, by Satish Kumar Keshri et al.
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients
by Satish Kumar Keshri, Nazreen Shah, Ranjitha Prasad
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Continual Federated Learning with Aggregated Gradients (C-FLAG) approach enables efficient, private, and scalable AI systems by addressing global catastrophic forgetting. This novel replay-memory based federated strategy combines edge-based gradient updates on memory and aggregated gradients on current data to minimize forgetting and bias while converging at a rate of O(1/√T) over T communication rounds. By formulating an optimization sub-problem that minimizes catastrophic forgetting, C-FLAG translates CFL into an iterative algorithm with adaptive learning rates for seamless task learning. Experimental results demonstrate C-FLAG’s superiority over state-of-the-art baselines in both task and class-incremental settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The holy grail of machine learning is to create a system that can learn from new data without forgetting old knowledge. This paper proposes a new way to do this called Continual Federated Learning with Aggregated Gradients (C-FLAG). C-FLAG uses a special kind of memory to help the system remember what it learned before, and then updates its learning based on new information. The authors show that this approach is better than other methods at keeping track of what was learned in the past while still being able to learn from new data. This has important implications for making AI systems more efficient, private, and scalable. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Optimization
